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Data-Enabled Predictive Control: In the Shallows of the DeePC
Open accessAbstract
We consider the problem of optimal trajectory tracking for unknown systems. A novel data-enabled predictive control (DeePC) algorithm is presented that computes optimal and safe control policies using real-time feedback driving the unknown system along a desired trajectory while satisfying system constraints. Using a finite number of data samples from the unknown system, our proposed algorithm uses a behavioural systems theory approach to learn a non-parametric system model used to predict future trajectories. The DeePC algorithm is shown to be equivalent to the classical and widely adopted Model Predictive Control (MPC) algorithm in the case of deterministic linear time-invariant systems. In the case of nonlinear stochastic systems, we propose regularizations to the DeePC algorithm. Simulations are provided to illustrate performance and compare the algorithm with other methods.
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Cites
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Islam, R., Henderson, P., Gomrokchi, M., & Precup, D. (2017) · arXiv · doi:10.48550/arxiv.1708.04133 · Cited by 177
Cited by
Learning-Based Model Predictive Control: Toward Safe Learning in Control
Hewing, L., Wabersich, K. P., Menner, M., & Zeilinger, M. N. (2020) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-090419-075625 · Cited by 835
Data-Driven Model Predictive Control With Stability and Robustness Guarantees
Berberich, J., Kohler, J., Muller, M. A., & Allgower, F. (2021) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2020.3000182 · Cited by 803
A quantitative framework for layered multirate control: Toward a theory of control architecture
Matni, N., Ames, A. D., & Doyle, J. C. (2024) · IEEE Control Systems · doi:10.1109/mcs.2024.3382388 · Cited by 39
Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic
Zhang, K., Chen, K., Li, Z., Chen, J., & Zheng, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3329484 · Cited by 24
Metadata
- Authors
- Coulson, J., Lygeros, J., & Dorfler, F.
- Venue
- 2019 18th European Control Conference (ECC)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 307-312
- OpenAlex
- W2900838060
- Cited by
- 628
- References
- 1
- FWCI
- 87.45
- Type
- conference paper
- Language
- en
- Open access
- green
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- Coulson, J., Lygeros, J., & Dorfler, F. (2019). Data-Enabled Predictive Control: In the Shallows of the DeePC. 2019 18th European Control Conference (ECC). https://doi.org/10.23919/ecc.2019.8795639
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